[pt] Este estudo investiga as habilidades linguísticas de crianças no processamento de estruturas linguísticas de alto custo. Foca-se nos possíveis efeitos do fechamento das escolas em decorrência da pandemia COVID-19 no desempenho de tarefas que envolvam estruturas de alto custo de processamento. Compara o desempenho linguístico de crianças de uma escola da rede pública de ensino do Rio de Janeiro em dois momentos: imediatamente após a reabertura das escolas, em 2022, após seu fechamento em decorrência da pandemia COVID-19 e um ano após a flexibilização do isolamento social, em 2023. Foram testados 100 escolares, com idade média de 9,5 anos e 8,8 anos, sem diagnóstico anterior de problemas de linguagem, por meio do módulo sintático da bateria MABILIN (Módulos de Avaliação de Habilidades Linguísticas), com vistas ao rastreio de dificuldades de linguagem, como as sugestivas do TDL (Transtorno do Desenvolvimento da Linguagem). No ano de 2022, das crianças testadas, 35,47 por cento apresentaram algum grau de dificuldade na compreensão das estruturas do MABILIN 1. Em contrapartida, em 2023, do total de crianças que apresentaram dificuldades em 2022, 61,1 por cento (22) não apresentaram mais dificuldades em 2023. Portanto, conclui-se ser possível que a pandemia COVID-19 tenha promovido dificuldades no desenvolvimento de habilidades linguísticas em escolares, embora não haja evidências claras de aumento da incidência de TDL. / [en] This study investigates schoolchildren s linguistic abilities in processing high-cost linguistic structures. It focuses on the possible effects of school closure due to the COVID-19 pandemic on the performance of tasks involving high-cost processing structures. The linguistic performance of children from a public school in Rio de Janeiro was compared at two different moments: immediately after the reopening of schools in 2022, after their closure due to the COVID-19 pandemic, and one year after the easing of social isolation, in 2023. One hundred students, with an average age of 9.5 years and 8.8 years, without prior diagnosis of language problems, were tested using the syntactic module of the MABILIN battery (Language Skills Assessment Modules), with a view to screening for language difficulties, such as those suggestive of DLD (Developmental Language Disorder). In 2022, 35.47 percent of the children tested presented some degree of difficulty in understanding the structures of MABILIN 1. This percentage differs considerably from what was obtained before the COVID-19 pandemic through the same battery of tests (7-9 percent), which is similar to what has been observed in different languages. In contrast, in 2023, of the total number of children who had difficulties in 2022, 61.1 percent (22) no longer had difficulties in 2023. Therefore, it is concluded that the COVID-19 pandemic may have promoted difficulties in the development of linguistic skills in schoolchildren, although there is no clear evidence of an increase in the incidence of DLD.

The disproportionate burden of covid-19 pandemic on racialized groups in developed countries has made socio-political and socio-economic inequalities even more apparent. This thesis utilizes critical race thoery (CRT), framing theory and the ”What’s the ’problem’ represented to be?”-approach to conduct a critical analysis of how the representation of the underlying causes of racial disparities in covid-19 mortality framed by the Swedish Public Health Agency. The published report on migrants and covid-19 ”Migrants and COVID-19 – Confirmed cases, ICU-cases and mortality from 13 March 2020 to 15 February 2021 among foreign-born in Sweden” is analyzed through qualitative content analysis. In the report, the Swedish Public Health Agency analyzes underlying causes to differences of covid-19 outcome based on country of birth, which suggests that the population born in other countries is affected by the covid-19 more than the population born in Sweden. The content analysis of the official document on foreign-borns and covid-19 mortality, released by the Swedish Public Health agency, suggests that the agency has represented the underlying causes of racial disparities in covid-19 mortality in Sweden with a socio-economic inequality frame, and from a CRT perspective, the representation is guided by colorblind ideology that does not problematize the role of racism in the society. The knowledge produced in this thesis aims to contribute to the field of CRT studies in Sweden with empirical knowledge about problematization of the covid-19 pandemic outcomes in Sweden

This study investigates the effects of the switch to online instruction in higher education in Sri Lanka after the COVID-19 outbreak. The study examines different aspects of the move to online education using a mixed-methods methodology that combines quantitative analysis of student satisfaction surveys and qualitative analysis of educator interviews. Qualitative study highlights the difficulties educators have in adjusting to digital platforms, the methods they use to improve student learning outcomes, and the advantages and disadvantages they see in online learning. A quantitative analysis looks at how satisfied students are with their online learning experiences and identifies the main variables that affect their engagement and academic success. For online education to be as effective as possible, the results highlight the necessity of continuous professional development for teachers, fair access to technology for students, and creative pedagogical strategies. In the context of post-pandemic higher education in Sri Lanka, the study adds to the body of literature by providing insights into the intricate interactions among technology, pedagogy, and social behaviors. Future study should focus on comparative evaluations of various online learning platforms, long-term studies to evaluate the effects of online learning, and examinations of challenges related to inclusion and digital equity.

By the end of 2020, the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), better known as COVID-19, had produced over 82,357,482 confirmed cases and 1,802,393 related deaths worldwide. The first pandemic many had ever experienced, the call of duty for public health professionals in the United States and globally was strong. While the contributions of doctors, nurses, and first responders are frequently acknowledged in the media and health-related publications, forgotten key players include health education specialists. Because of their critical role in public health preparedness and response, special attention should be given to their work during disasters and public health emergencies. As the Screening Manager of the New Jersey Department of Health (DOH) COVID-19 Testing Center located at Bergen Community College, I observed firsthand how COVID-19 made people suffer, but also the resilience among health education specialist responders. Using narrative inquiry to detail my response experience, this dissertation offers the world my story, while highlighting the onset of the pandemic in New Jersey from March to June 2020 and the practice of incorporating relevant health education competencies.

Psychiatric nurse practitioner (NP) turnover is a significant cost to healthcare employers. Psychiatric NP turnover due to private practice creation is not well understood. The purpose of this study was to better understand the transition of Psychiatric NPs as they moved from ambulatory/outpatient employment to private practice. Using a hybrid Grounded Theory approach, 17 Psychiatric NPs in private practice were interviewed from October - December 2023. Facilitating factors to private practice creation were: The COVID-19 pandemic, changes to telehealth regulation during the pandemic, increased demand for psychiatric services and decreased supply of providers, and progress within the NP full practice authority movement. While employed, NPs reported experiencing burnout, high patient load, poor schedule, poor compensation, increased administrative tasks not budgeted within the NP workday, and rampant disrespect of the NP role. NPs reported being managed by inappropriate supervisors from other disciplines, and reported little opportunity for growth. Most participants voiced an initial reluctance to enter private practice, and reported being “pushed” to entrepreneurship out of concern for their long term wellbeing. These factors combined contributed to poor NP job satisfaction and NP turnover. Strategies to improve the job satisfaction of employed psychiatric NPs were recommended.

Das Ziel der vorliegenden Studie ist es, abweichendes Verhalten in der COVID-19 Pandemie zu erklären und empirisch zu erforschen. Genauer liegt der Fokus auf Determinanten von Impfnachweisfälschungen und des Lügens in Bezug auf den eigenen Impf- und Teststatus. Es werden Daten einer Online-Befragung ausgewertet (n = 549). Mit Blick auf die Erklärung des abweichenden Verhaltens, können die aus der Rational Choice Theory abgeleiteten Hypothesen zur Sanktionswahrscheinlichkeit und Sanktionshöhe empirisch nicht bestätigt werden. Die Hypothesen zum Einfluss von sozialen Normen sind dagegen robuster. So ist das abweichende Verhalten im eigenen sozialen Umfeld ein guter Prädiktor für die eigene Impfnachweisfälschung. Zudem verhält sich eine Person eher dann abweichend, wenn sie glaubt, dass sich die Mehrheit der Gesellschaft nicht an Corona-Restriktionen hält. Schließlich zeigt die Überprüfung einer Reihe von Einstellungshypothesen, dass das Misstrauen in das Robert Koch-Institut ein robuster Prädiktor für die eigene Devianz ist. / The aim of the present study is to theoretically explain and empirically investigate deviant behavior in the COVID-19 pandemic. More specifically, our study focuses on vaccination certificate falsification and lying about one's own vaccination and test status. Data of an online survey is analyzed (N = 549). In regards to the explanation of deviant behavior, the hypothesized effects of the probability of sanctions and the severity of sanctions derived from the rational choice theory cannot be confirmed empirically. The hypotheses about the influence of social norms, on the other hand, are more robust. Deviant behavior in one's own social network is a good predictor of the falsification of vaccination certificates. In addition, deviant behavior is more likely if a person believes that the majority of society is not adhering to Corona restrictions. Finally, testing a series of attitudinal hypotheses shows that distrust in the Robert Koch Institute is a robust predictor of deviant behavior.

Background: The COVID-19 pandemic posed an unparalleled challenge to worldwide public health systems, characterized by its high transmissibility and the initial absence of accessible testing, treatments, and vaccines. The deficiency in public awareness and the scarcity of readily available public health information regarding this century's disaster further intensified the critical need for innovative solutions to bridge these gaps. In response, Shaman Labs1,2, leveraging its deep expertise in forecasting for influenza3, Ebola, and various SARS viruses, initiated the development of country-wide COVID-19 projections within weeks following the WHO's declaration of the pandemic4–6. Almost immediately thereafter, it became necessary to create a sophisticated online platform—a system capable of displaying county-specific COVID-19 forecasts, including daily estimated infections, cases, and deaths. This platform was designed to allow users to select any county, state, or national geography and compare it with another, under various scenarios of social distancing measures. Additionally, the architecture of this system was required to facilitate the regular integration of updated data, ensuring the tool's ongoing relevance and utility. Columbia University's data visualization system aimed to communicate epidemiological forecasts to various stakeholders. At the onset of the COVID-19 pandemic, amid escalating uncertainty and the pressing need for reliable data, Dr. Rundle played a pivotal role in briefing key stakeholders on the unfolding crisis. His efforts were directed towards providing Congressman Ron Johnson, Chairman of the U.S. Senate Committee on Homeland Security & Governmental Affairs, and Congresswoman Anna Eshoo, as well as their staff, with up-to-date projections and analyses derived from the Classic Data Visualization tools. Dr. Rundle’s consultative role extended to a diverse array of institutions including the U.S. Army Corps of Engineers, the U.S. Air Force, and the Federal Reserve Board, as well as advising private entities such as Pfizer, MetLife, and Unilever. His expertise facilitated informed planning and response efforts across various levels of government and sectors, underscoring the critical role of sophisticated data visualization from the earliest stages of the pandemic. This Integrated Learning Experience (ILE) examines the development and implementation of the Time Machine platform, focusing on its application in visualizing and analyzing COVID-19 epidemiological forecasts. The study explores methods for improving forecast data presentation, analysis, and accuracy assessment. Methods: The body of this work unfolds through a series of critical chapters that collectively address the multifaceted functionality and impact of the Time Machine platform. Initially, the work focuses on the construction of the Time Machine platform, a web-based R interactive user interface coupled with cloud-based database system, specifically tailored for the intuitive visualization of epidemiological forecasts, detailing the technical and design considerations essential for enabling users to interpret complex data more effectively. Following this, the implementation of a rigorous data-discovery framework is presented, examining case reporting inconsistencies across different regions, using low-level GitHub and Windows scripting technologies, thereby highlighting the significance of accurate data collection and the impact of discrepancies on public health decisions. The narrative then transitions to the implementation of advanced statistical models, such as strictly proper scoring and weighted interval scoring, to assess the accuracy of the forecasts provided by the Time Machine platform, using a dedicated R library and testing with the help of MS Excel sandbox, underscoring the importance of reliable predictions in the management of public health crises. Lastly, a detailed analysis is conducted, encompassing countrywide data (3142 counties) over an extended period (147 weeks), utilizing Generalized Estimating Equations (GEE) to identify key predictors that influence forecast accuracy, offering valuable insights into the factors that either enhance or detract from the reliability of epidemiological predictions. Results: The deployment of the Classic Data Visualization and the subsequent evolution of the Time Machine platform have significantly advanced epidemiological forecast visualization capabilities. The Time Machine platform was designed with an automated data refresh system, allowing for regular updates of epidemiological forecast data and reported actuals. The project developed tools for monitoring and evaluating the quality of public health reporting, aiming to improve the accuracy and timeliness of data used in public health decisions. Additionally, the research implemented methods for standardizing forecast accuracy assessments, including the normalization of scores to enable comparisons across different geographical scales. These approaches were designed to support both local and national-level pandemic response efforts. The accuracy analyses throughout different phases of the pandemic revealed a 42% improvement in forecast accuracy from Phase 1 to Phase 7. Larger populations (27% increase per unit increase on a base-10 logarithmic scale) and higher county-level activity (45% increase from the lowest to the highest quartile) resulted in better estimations. Additionally, the analysis highlighted the significant impact of reporting quality on forecast accuracy. On the other hand, the study identified the challenges in predicting case surges, showing a 27% decline in accuracy during periods of rising infections compared to declining periods. The regression results highlight the potential benefits of improving data collection and providing timely feedback to forecasting teams. Conclusion: This study demonstrates the potential of advanced data visualization and accuracy measurement techniques in improving epidemiological forecasting. The findings suggest that factors such as urbanicity, case reporting quality, and pandemic phase significantly influence forecast accuracy. Further research is needed to refine these models and enhance their applicability across various public health scenarios.

In marketing and other social sciences, researchers often use field data to empirically study how people make decisions in naturalistic environments. There are numerous theoretical and practical challenges to doing so, and in this dissertation, I propose methodological approaches to address two such challenges. First, people often make complex decisions that are described in terms of high-dimensional or unstructured variables (e.g., writing text or choosing an assortment from a large set of options) which are difficult to analyze relative to simpler decisions (e.g., binary choices). Second, when analyzing how people's decisions are affected by a major event (e.g., regulatory changes or a global pandemic), events often affect a large population of interest simultaneously, making it difficult to assess the impact of the event relative to a counterfactual where the event did not occur. In Chapter 1, I address the first challenge in the context of non-cooperative games. I develop a novel neural network architecture that enables behavioral analysis of complex games by estimating a game's payoff structure (e.g., win probabilities between pairs of actions) while simultaneously mapping agent actions to a lower-dimensional latent space that encodes strategic similarities between actions in a smooth, linear manner. I apply my method to analyze a unique dataset of over 11 million matches played in a competitive video game with a large array of actions and complex strategic interactions. I find that players select actions that counterfactually would have performed better against recent opponents, demonstrating model-based reasoning. Still, players overrely on simple heuristics relative to model-based reasoning to an extent that is similar to findings reported in lab settings. I find that noisy and biased decision-making leads to frequent selection of suboptimal actions, which corresponds to lower player engagement. This demonstrates the limits of player sophistication when making complex competitive decisions and suggests that platforms hosting competitions may benefit from interventions that enable players to improve their decision-making. In Chapter 2, I address the second challenge, proposing a general and flexible methodology for inferring the time-varying effects of a discrete event on consumer behavior when the event spans the target population being analyzed, such that there is no contemporaneous "control group" and/or it is not possible to measure treatment status. I achieve identification by exploiting the empirical regularity of customer spending patterns across cohorts (i.e., groups of customers who adopted the same product or service at different times), comparing purchasing behavior across cohorts who were affected by the event at different points in their tenure. My method applies nonparametric age-period-cohort (APC) models, commonly used in sociology but with limited adoption in marketing, in conjunction with a predictive model of the counterfactual no-event baseline (i.e., an event study model). I use this method to infer how the COVID-19 pandemic has affected 12 online and offline consumption categories. My results suggest that the pandemic initially drove significant spending lifts at e-commerce businesses at the expense of brick-and-mortar alternatives. After two years, however, these changes have largely reverted. I observe significant heterogeneity across categories, with more persistent changes in subscription-based categories and more transient changes in categories based on discretionary purchases, especially those of durable goods.

Background: The status of Safety Management is highly relevant to evaluate an organization’s ability to deal with unexpected events or errors, especially in times of crisis. However, it remains unclear to what extent Safety Management was developed and suffciently implemented within the healthcare system during the COVID-19 pandemic. Providing insights of potential for improvement is expected to be directional for ongoing Safety Management efforts, in times of crisis and beyond. Method: A nationwide survey study was conducted among healthcare professionals and auxiliary staff on German Intensive Care Units (ICUs) evaluating their experiences during the first wave of the COVID-19 pandemic. Error Management and Patient Safety Culture (PSC) measures served to operationalize Safety Management. Data were analyzed descriptively and by using quantitative content analysis (QCA). Results: Results for n = 588 participants from 53 hospitals show that there is a gap between errors occurred, reported, documented, and addressed. QCA revealed that low quality of safety culture (27.8%) was the most mentioned reason for errors not being addressed. Overall, ratings of PSC ranged from 26.7 to 57.9% positive response with Staffng being the worst and Teamwork Within Units being the best rated dimension. While assessments showed a similar pattern, medical staff rated PSC on ICUs more positively in comparison to nursing staff. Conclusion: The status-analysis of Safety Management in times of crisis revealed relevant potential for improvement. Human Factor plays a crucial role in the occurrence and the way errors are dealt with on ICUs, but systemic factors should not be underestimated. Further intensified efforts specifically in the fields of staffng and error reporting, documentation and communication are needed to improve Safety Management on ICUs. These findingsmight also be applicable across nations and sectors beyond the medical field.

South Asians are the third fastest growing racial/ethnic minority group in the United States with distinct cultural characteristics. The coronavirus disease 2019 (COVID-19) pandemic has disproportionately impacted racial and ethnic minorities in the U.S, including South Asians, across several life domains: work, home life/education, social activities, economic, emotional and physical health, infection, quarantine, and positive changes. The COVID-19 pandemic may have critically impacted South Asians with traumatic event experiences and posttraumatic stress disorder (PTSD) symptom severity across several life domains. Limited work suggests high rates of interpersonal traumas and substantial PTSD symptom severity in the South Asian community. Uniquely, the current study examined which life domains impacted by the COVID-19 pandemic associated with a greater count of traumatic event types, interpersonal vs. non-interpersonal traumas, and PTSD symptom severity. Results revealed that negative experiences in social activities, as well as distress in economic, emotional, and physical health domains, were significantly associated with the count of traumatic event types. Negative social activity experiences, and distress in the economic and emotional health domains, were also significantly associated with PTSD symptom severity. Quarantine and physical health domains significantly associated with the count of interpersonal traumas, while COVID-19-related experiences (in social, quarantine, and infection domains) significantly associated with the count of non-interpersonal traumas. Findings inform clinically relevant pandemic research in a vulnerable population and provide trauma and PTSD prevalence estimates in the South Asian community.